This research highlights the results obtained from applying the method of inverse kinematics, using Groebner basis
theory, to the human gait cycle to extract and identify lower extremity gait signatures. The increased threat from suicide
bombers and the force protection issues of today have motivated a team at Air Force Institute of Technology (AFIT) to
research pattern recognition in the human gait cycle. The purpose of this research is to identify gait signatures of human
subjects and distinguish between subjects carrying a load to those subjects without a load. These signatures were
investigated via a model of the lower extremities based on motion capture observations, in particular, foot placement and
the joint angles for subjects affected by carrying extra load on the body. The human gait cycle was captured and
analyzed using a developed toolkit consisting of an inverse kinematic motion model of the lower extremity and a
graphical user interface. Hip, knee, and ankle angles were analyzed to identify gait angle variance and range of motion.
Female subjects exhibited the most knee angle variance and produced a proportional correlation between knee flexion
and load carriage.